主题建模简介

R 自然语言处理入门

Kasey Jones

Research Data Scientist

主题建模

体育新闻：

scores 赛况
球员八卦
球队新闻
等等

赞比亚天气：

潜在狄利克雷分配

文档是主题的混合
- 团队新闻 70%
- 球员八卦 30%
主题是词的混合
- 团队新闻：trade, pitcher, move, new
- 球员八卦：angry, change, money

报纸充满了聚焦特定主题的文章。

¹ https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation

LDA 前的准备

准备语料：

animal_farm_tokens <- animal_farm %>%
  unnest_tokens(output = "word", token = "words", input = text_column) %>%
  anti_join(stop_words) %>%
  mutate(word = wordStem(word))

文档-词项矩阵：

animal_farm_matrix <- animal_farm_tokens %>%
  count(chapter, word) %>%
  cast_dtm(document = chapter, term = word,
           value = n, weighting = tm::weightTf)

LDA

library(topicmodels)
animal_farm_lda <- LDA(train, k = 4, method = 'Gibbs',
                 control = list(seed = 1111))
animal_farm_lda

A LDA_Gibbs topic model with 4 topics.

LDA 结果

animal_farm_betas <-
    tidy(animal_farm_lda, matrix = "beta")
animal_farm_betas

# A tibble: 11,004 x 3
   topic term         beta
   <int> <chr>       <dbl>
...
 5     1 abolish 0.0000360
 6     2 abolish 0.00129  
 7     3 abolish 0.000355 
 8     4 abolish 0.0000381
...

sum(animal_farm_betas$beta)

[1] 4

每个主题的高频词

animal_farm_betas %>%
  group_by(topic) %>%
  slice_max(beta, n = 10) %>%
  arrange(topic, -beta) %>%
  filter(topic == 1)

   topic term          beta
   <int> <chr>        <dbl>
 1     1 napoleon   0.0339 
 2     1 anim       0.0317 
 3     1 windmil    0.0144 
 4     1 squealer   0.0119 
 ...

animal_farm_betas %>%
  group_by(topic) %>%
  slice_max(beta, n = 10) %>%
  arrange(topic, -beta) %>%
  filter(topic == 2)

   topic term        beta
   <int> <chr>      <dbl>
...
 3     2 anim     0.0189 
 ...
 6     2 napoleon 0.0148 
 ...

高频词（续）

我们可以按主题列出高频词，并用 ggplot 可视化。

¹ https://www.tidytextmining.com/topicmodeling.html

将文档标注为主题

animal_farm_chapters <- tidy(animal_farm_lda, matrix = "gamma")
animal_farm_chapters %>%
  filter(document == 'Chapter 1')

# A tibble: 4 x 3
  document  topic  gamma
  <chr>     <int>  <dbl>
1 Chapter 1     1 0.157 
2 Chapter 1     2 0.136 
3 Chapter 1     3 0.623 
4 Chapter 1     4 0.0838

LDA 练习！

R 自然语言处理入门